good morning good morning good morning we're going home to Denmark and boy did
I have trouble finding a plane apparently the company I bought the
ticket with went under I had to buy new tickets yesterday that's expensive
please just take it
I've taken the wrong train brilliant I'm somewhere in Auckland today I thought we
would talk about this are we headed toward a future without jobs the fear
that automation might displace workers and potentially lead to lots of
unemployment goes back at a minimum 200 years and since then this concern has
come up again and again the culture organisation put out an alert we have a
billion people on the verge of hunger and starvation every technology
revolution would call social instability the first - technology revolution I've
said calls the water world one second technology revolution cost war - this is
the third technology revolution people already unhappy because a lot of machine
learning artificial intelligence killing lot of job people start to worry if
you're not innovative enough if you're not like a creative enough your job will
be taking away by a lot of machines on artificial intelligence front i have
exposure to the very most cutting-edge AI and i think people should be really
concerned about it ai is a rare case where I think we need to be proactive in
regulation instead of reactive because I think by the time we are reactive in AI
regulation it's too late and I do think that when the computer takes over
certain tasks that'll be tough for us it'll be a long time before you're
matching the type of broad judgment that humans exercising in many different
areas there's no institution in the world that
cannot be improved with machine learning we live in a very interesting time
because there are a few golden ages happening and one of them is machine
learning and we're doing everything with it we're grading strawberries with it
you know for Amazon fresh I'm gonna guess that most of you have probably
never heard of the triple revolutionary pork and this report was presented to
the President of the United States and it argued that the US was on the brink
of economic and social upheaval because industrial automation was going to put
millions of people out of work now that report was delivered to President Lyndon
Johnson in March of 1964 that's now over 50 years and of course that hasn't
really happened and that's been the story again and again
knowledge he made devastate entire industries it may wipe out whole
occupations and types of work but at the same time progress is going to lead to
entirely new things so there will be new industries there'll be new kinds of work
that will appear and that has been the story so far and it's been a positive
story but there is one particular class of worker for whom this story has been
quite different for these workers technology has completely decimated
their work and it really hasn't created any new opportunities at all and these
workers of course our horses is it possible that at some point in the
future a significant fraction of the human workforce is going to be made
redundant in the way that horses were you might say that's absurd how can you
possibly compare human beings to horses we can learn we can adapt and in theory
that ought to mean that we can always find something new to do and if we can
always remain relevant to the future economy here's the really critical thing
to understand the future is going to be full of thinking learning adapting
machines we see a world a nanosecond from here where data enables machine
learning understanding through repetition and recognitions adapting and
adjusting what that really means is that
technology is finally beginning to encroach on that fundamental human
capability the very thing that so far has allowed us to stay ahead of the
march of progress and remain relevant and in fact indispensable to the economy
if you wanted to get a computer to do something new you would have to program
in excruciating detail every single step that you want the computer to achieve
now if you want to do something that you don't know how to do yourself and this
is going to be a great challenge this man Arthur Samuel wanted to get this
computer to be able to beat him at checkers so he came up with an idea he
had the computer play against itself thousands of times and learn how to play
checkers networked and in fact by 1962 this computer had beaten the Connecticut
state champion so after samuel was the father of machine learning the first big
success of machine learning commercially was google google showed that it is
possible to find information by using a computer algorithm since that time there
has been many commercial successes of machine learning so we now know that
computers can learn and computers can learn to do things that we actually
sometimes don't know how to do ourselves a team won a competition for automatic
drug discovery they beat all of the algorithms developed by Merck or the
International academic community nobody on the team had any background in
chemistry or biology or life sciences and they did it in two weeks how did
they do this they used an extraordinary algorithm called deep learning deep
learning is an algorithm inspired by how the human brain works and as a result
it's an algorithm which has no theoretical limitations on what it can
do the more data you give it and the more computation time you give it the
better it gets from many Chinese speakers and produce a text-to-speech
system we've taken an hour or so up my own voice and we use that to modulate
the standard text to speech system so that it would sound like me again the
results are perfect there are back when engineers did what people had made
deep learning is this extraordinary thing it's a single algorithm that can
seem to do almost anything in this obscure competition from Germany called
the German traffic sign benchmark deep learning had learned to recognize
traffic signs not only could it recognize the traffic signs better than
any other algorithm the leaderboard actually showed it was better than
people about twice as good as people by 2011 we had the first example of
computers that can see better than people since that time a lot has
happened in 2012 Google announced that they had a deep learning algorithm watch
YouTube videos the computer independently learned about concepts
such as people and cats this is much like the way that humans learn humans
don't learn by being told what they see but by learning for themselves what
these things are Google announced last year that they had mapped every single
location in France in two hours and the way they did it was that they fed street
view images into a deep learning algorithm to recognize street numbers
imagine how long it would have taken before dozens of people many years in
Stanford a grip there announced that looking at tissues under magnification
they've developed a machine learning based system which in fact is better
than human pathologists at predicting survival rates for cancer sufferers the
computer system discovered that the cells around the cancer as important as
the cell cancer cells themselves in making a diagnosis this is the opposite
of what pathologists had been taught for decades so what does it mean now that
computers can see there's a lack of medical expertise in the world it would
take about 300 years to train enough people to fix that problem so imagine if
we can help enhance their efficiency using these deep learning approaches so
I'm very excited about the opportunities I'm also concerned about the problems
the problem here is that every area where in blue on this map is somewhere
where services are over 80 percent of employment what are services these are
services these are also the exact things that computers have just learned how to
do 80% of the world's employment in the developed world is stuff that computers
have just learnt how to do what does that mean well it'll be fine or be
replaced by other jobs for example there'll be more jobs for data
scientists well not really it doesn't take data scientists very
long to build these things for example these four algorithms were all built by
the same guy if you think oh it's all happened before
we've seen the results in the past of where new things come along and they get
replaced by new jobs it's very hard for us to estimate this because human
performance grows at this gradual rate but we now have a system deep learning
that we know actually grows in capability exponentially and we're here
in five years time computers will be off this chart we have seen this once before
of course in the Industrial Revolution we saw a step change in capability
thanks to engines the thing is though that after a while things flattened out
once engines were used to generate power in all the situations things really
settled down the machine learning revolution is going to be very different
to the Industrial Revolution because the machine learning revolution never
settles down so what is it that is really so different about today's
information technology relative to what we've seen in the past I would point to
three fundamental things and the first thing is exponential acceleration now I
know you all know about Moore's law but in fact it's more broad-based than that
it extends in many cases for example to software it extends to communications
bandwidth and so forth the second key thing is that the machines are in a
limited sense beginning to think and by this I don't mean human level artificial
intelligence I simply mean that machines and algorithms are solving problems and
most importantly they're learning machine learning which is just becoming
this incredibly powerful disruptive scalable technology we tend to draw a
very distinct line and on one side of that line are all the jobs and tasks
that we perceive as being on some level fundamentally routine and repetitive and
predictable because they are innately predictable we know that they're
probably at some point going to be susceptible to machine learning and
therefore to automation that's a lot of jobs that's probably something on the
order of roughly half the jobs in the economy but then on the other side of
that line we have all the jobs that require some capability that we perceive
as being uniquely human and these are the jobs that we think are safe now
based on what I know about the game of Go I would have guessed that it really
ought to be on the safe side of that line but the fact that it isn't and that
Google solved this problem suggests that that line is going to be very dynamic
it's going to shift in a way that consumes more and more jobs that we
currently perceive as being safe from automation this is by no means just
about low-wage job a blue-collar job has lots of evidence
to show that these technologies are rapidly climbing the skills ladder so as
we put these trends together I think what it shows is that we could very well
end up in a future with significant unemployment so a fundamental economic
problem because jobs are currently the primary mechanism that distributes
income and therefore purchasing power to all the consumers in order to have a
vibrant market economy you've got to have lots and lots of consumers if you
don't have that then you run the risk of economic stagnation or maybe even a
declining economic spiral as there simply aren't enough customers out there
to buy the products and services being produced so the question then becomes
what exactly could we do about this and I think you can view this through a very
utopian framework but at the same time I think we have to be realistic and we
have to realize that we're very likely to face a significant income
distribution problem a lot of people are likely to be left behind I think that in
order to solve that problem we're ultimately going to have to find a way
to decouple incomes from traditional work in the best way I know to do that
is some kind of a guaranteed income or universal basic income my own view is
that a basic income is not a panacea it's not necessarily a plug-and-play
solution rather it's a place to start it's an idea that we can build on and
refine for example one thing that I have written quite a lot about is the
possibility of incorporating explicit incentives into a basic income imagine
that you are a struggling high school student suppose you know that at some
point in the future no matter what you're gonna get the same basic income
as everyone else in my mind that creates a very perverse incentive for you to
simply give up and drop out of school I would say let's not structure things
that way instead pay people who graduate from high school somewhat more than
those who simply drop out and we can take that idea of building incentives
into a basic income and maybe extend it to other areas by incorporating
incentives into a basic income we might actually improve it and also perhaps
take at least a couple of steps towards solving another problem that I think
we're quite possibly gonna face in the future and that is how do we all find
meaning and fulfilment and how do we occupy our time in a world where perhaps
there's less demand for traditional work refining a basic income and make it look
better also perhaps make it more politically
and socially acceptable and feasible I think one of the most kind of
fundamental objections to many of us is this feared that we're gonna end up with
too many people riding in the economic cart and not enough people pulling that
car and yet really the whole point I'm making here of course is that in the
future machines are increasingly going to be capable of pulling that cart for
us that should give us more options for the way we structure our society and our
economy and I think that eventually it's going to go beyond simply being an
option and it's gonna become an imperative because jobs are that
mechanism that gets purchasing power to consumers so that they can then drive
the economy if that mechanism begins to erode in the future and we're going to
need to replace it with something else or we're gonna face the risk that our
whole system simply may not be sustainable the bottom line here is that
I really think that solving these problems finding a way to build a future
economy that works for everyone at every level of our society is going to be most
one of the most important challenges that we all face in the coming years and
decade
I'm gonna come to machine learning there's really two views either it'll
mess up our entire society they'll certainly be a lot of job destruction
here's what's gonna happen is robust will be able to do everything better
than us I mean I'm quitting I mean all of us you know I really think we need to
go a regulation here you're ensuring the public good a so cuz you've got
companies that are racing that they kind of have to race to build AI or they're
gonna be made uncompetitive you know elected essentially if your competitor
is Racing's about AI and you don't they will crush you or it won't really do
much I'm really optimistic Braylon optimistic person in general I
think you can build things and and the world gets better with AI especially I'm
really optimistic I'm a little more conservative than Mark Zuckerberg but
I'm definitely not a hundred percent on Elon Musk's side I do believe it is
important for us to draw a line and sort of see what
industries will be disrupted at which ones won't I'm also not on Mark
Zuckerberg side I really do think that especially when machine learning becomes
super intelligent and it sort of becomes an AI we have to keep an eye on it it's
really interesting let me know what do you think going to London so the most
prominent arguments that really came up was that I was a communist or that
really was gonna save us and I just want to point out that politics have
absolutely nothing to do with this we're not talking about what we want to have
happen we're talking about what we think will happen because of how all of the
numbers are trending we don't know if it will happen in the next 20 years
when Elon Musk says 20 years that's kind of because he's Elon Musk just want to
make that sure basic economics state that scarcity is a thing and we kinda
have to talk about that
home sweet home I don't think I agree with people like
Max Zuckerberg or Elon Musk as I feel like that's a little bit extreme I think
we have to draw a line in the middle some industries will be disrupted some
won't but yeah the really interesting part about this is the conversation so
tell me what you guys think in the comments down below and yeah subscribe
to the channel if you're new let me know what you think about this vlog you
mentoree I'll see you guys next week take care
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